Evolutionary multi-objective optimization for multivariate pairs trading.

• First large-scale framework to accommodate multivariate pair formation. • Formulates pair formation as a Mixed Integer Programming model. • NSGA-II simultaneously optimizes volatility and mean-reversion. • Details genetic algorithm alterations for implementation. • Significantly outperforms benchmark strategies. Forming combinations of comoving assets is a critical step in pairs trading that has only been addressed either manually or through enumerative procedures. Both approaches fail in the multivariate case and do not consider conflicting objectives in the problem structure. This paper is the first attempt to address these novel problems by presenting an intelligent system that recommends profitable pair combinations through a Mixed Integer Programming (MIP) formulation and solving the NP-Hard optimization problem with a multi-objective genetic algorithm (NSGA-II) containing problem specific modifications. Combinations of assets are optimized on two conflicting objectives of risk (mean-reversion) and return (spread variance) to form sets of profitable multivariate pairs trading opportunities. Promising results support the superiority of multi-objective and multivariate pairs trading strategies over their traditional single objective and univariate counterparts. The findings should motivate new directions for pairs trading research and also expand the applications of evolutionary multi-objective optimization for hard problems in finance and other industries. [ABSTRACT FROM AUTHOR]